Open AccessLinear minimum‐mean‐squared error estimation of phase noise, which has a symmetric Levy distribution and a possibly large magnitude, from observables at irregular instants
Author(s) -
Su YeongTzay,
Song Yang,
Wong Kainam Thomas
Publication year - 2013
Publication title -
iet communications
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.355
H-Index - 62
eISSN - 1751-8636
pISSN - 1751-8628
DOI - 10.1049/iet-com.2013.0144
Subject(s) - estimator , mathematics , independent and identically distributed random variables , observable , noise (video) , mean squared error , inversion (geology) , minimum mean square error , statistics , random variable , computer science , physics , paleontology , quantum mechanics , structural basin , image (mathematics) , biology , artificial intelligence
This study extends an algorithm, previously proposed by the present authors, for ‘linear minimum‐mean‐squared error’ estimation of phase noise of (possibly) temporal non‐stationarity, large magnitude, ‘non’‐identical increments that have a Levy distribution, of which the Wiener distribution represents a special case. This estimator‐taps may be pre‐set to any number, may be pre‐computed offline with no matrix inversion, based on the prior knowledge of only the signal‐to‐(additive)‐noise ratio and the phase‐noise's characteristic function. That estimator may be set to various degrees of latency. This is here generalised to allow observables at irregular time‐instants (e.g. because of the irregular placement of pilot symbols in the transmitted waveform), under which the phase‐noise increments become non‐identically distributed. This study handles this more complicated scenario.